Acclimation to daily thermal variability drives the metabolic performance curve
2013•repositorio.uchile.cl
Background: Among the predictions of the effect of future climate change, the impact of
thermal conditions at local levels on the physiological performance of individuals and their
acclimation capacities is key to understanding animals' responses to global warming. Goal:
Test for the effect of acclimation to environmental thermal variability, namely 24±0 C, 24±4
C, and 24±8 C, on the metabolic performance curve in the mealworm beetle Tenebrio
molitor. Results: Maximum resting metabolism and metabolic breadth were significantly …
thermal conditions at local levels on the physiological performance of individuals and their
acclimation capacities is key to understanding animals' responses to global warming. Goal:
Test for the effect of acclimation to environmental thermal variability, namely 24±0 C, 24±4
C, and 24±8 C, on the metabolic performance curve in the mealworm beetle Tenebrio
molitor. Results: Maximum resting metabolism and metabolic breadth were significantly …
Background
Among the predictions of the effect of future climate change, the impact of thermal conditions at local levels on the physiological performance of individuals and their acclimation capacities is key to understanding animals’ responses to global warming.
Goal
Test for the effect of acclimation to environmental thermal variability, namely 24 ± 0 C, 24 ± 4 C, and 24 ± 8 C, on the metabolic performance curve in the mealworm beetle Tenebrio molitor.
Results
Maximum resting metabolism and metabolic breadth were significantly different, but optimal temperature was similar, between treatments. Thus, increases in ambient thermal variability caused a reduction in maximum performance at each level of acclimation, with a decrease of almost 50% between the nil variability and ± 8 C daily variability treatments.
Conclusions
If thermal variability changes in any of the directions forecast by climatologists, ecologists will have to use mechanistic and modelling approaches based on physiological and biophysical traits to predict the biodiversity consequences of climate change.
repositorio.uchile.cl
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